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Evaluation of Interestingness Measures for Ranking Discovered Knowledge [chapter]

Robert J. Hilderman, Howard J. Hamilton
2001 Lecture Notes in Computer Science  
We theoretically and empirically evaluate thirteen diversity measures used as heuristic measures of interestingness for ranking summaries generated from databases.  ...  We then analyze the distribution of the index values generated by e a c h of the thirteen diversity measures.  ...  Future research will focus on extending the theory of interestingness for diversity measures used to rank summaries.  ... 
doi:10.1007/3-540-45357-1_28 fatcat:wllp6tyzqffibl5oq6c6koahaq

A survey of interestingness measures for knowledge discovery

2005 Knowledge engineering review (Print)  
We evaluate the strengths and weaknesses of the various interestingness measures with respect to the level of user integration within the discovery process.  ...  These so called interestingness measures are generally divided into two categories: objective measures based on the statistical strengths or properties of the discovered patterns and subjective measures  ...  , calculates the probabilities associated with the data (Lorenz, 1905) q m i=1 (m − i + 1)P i Gini inequality measure based on the Lorenz curve, uses the ratio of the Lorenz curve and the total area  ... 
doi:10.1017/s0269888905000408 fatcat:7aiqi4oacvd4hd2sdt2cdqh3na

Access Method [chapter]

2017 Encyclopedia of GIS  
Model generalization is also called geodatabase abstraction, as it relates to generating a more simple digital representation of geometric objects in a database, leading to a considerable data reduction  ...  of databases; Geographic data reduction; Model generalization; Multiple resolution database Definition Model generalization is used to derive a more simple and more easy to handle digital representation  ...  The points that may affect the dominance-sum query of a query point in are those dominated by the upper-right point of  ... 
doi:10.1007/978-3-319-17885-1_100021 fatcat:ruj53dunmjhihjmn2svftoof2a

Boundless data analytics through progressive mining

We define an attribute-value pair from a dimension as a descriptor, and a conjunction of k descriptors is used to slice a dataset.  ...  The problem of generating all possible large data slices is formalized as the frequent itemset mining problem.  ...  An example is shown in Figure 3 .3e. Score the Plots As mentioned in the opening of this chapter, we intend to find those "interesting" plots. How to measure the interestingness of a plot?  ... 
doi:10.7282/t3-jse5-ng63 fatcat:x5ptmogzhrdbbnnarti5lktgwy